Reference-Free Machine Vision Inspection of Semiconductor Die Images

Advances in electronic technology have made integrated circuits (ICs) the fundamental components in all electronic devices. To increase their production yield by catching defects as early as possible, we need to perform quality assurance on the semiconductor dies during the assembly and packaging processes. A common approach is to employ machine vision to compare a test die with a "known good die". However, difficulties in ensuring identical imaging conditions (such as illumination) are limitations to this die-to-die comparison approach. Instead, in this work we develop a novel reference-free defect detection algorithm for an IC die by analyzing its image. By identifying intrinsic and extrinsic features of various segments in the image, we implement a classification scheme to identify whether the die is defective or not. We rely on the fact that normal ICs contain regular patterns, and the abnormal and irregular regions are classified as potential areas of defects. Experimental results show that the proposed reference-free defect detection algorithm can detect most of the defects from different types of IC dies, and can also correctly classify normal IC dies as non-defective. These results demonstrate the feasibility of the reference-free defect detection approach.